An accurate technique for gunshot detection can provide needed assistance to law enforcement agencies and have a positive impact on crime control. Gunshot recordings may be used for tactical detection and forensic evaluation to ascertain information about the type of firearm and ammunition employed.
Accurate gunshot detection and categorization analysis are subject to a number of significant challenges. Perhaps the most significant challenge is the effect of recording conditions on an audio signature of recorded data. Recording conditions include variations in capture conditions and factors stemming from the mechanics of a gun. For example, a muzzle blast is the primary sound emanation from sub-sonic bullets shot from a weapon, which is influenced by ammunition characteristics, gun barrel length, as well as the presence of acoustic suppressors that disguise the weapon. The mechanical action of the weapon is picked up only if a microphone is close to the weapon. For supersonic bullets, a shock wave precedes the muzzle blast and is comparably strong in signal power. As a result, even a single bullet produces pairs of sounds. Propagation through the ground or other solid surfaces becomes relevant when the recording device is close to the weapon. The speed of sound may be five times higher in solid media than in air.
A second set of challenges to effective gunshot detection and categorization analysis is lossy propagation and reflection of sound from a fired weapon. Variations in temperature, humidity, ground surfaces, and obstacles directly influence the extent of attenuation and scattering. Wind direction may affect the perceived frequency of a gunshot. These effects are not significant at a distance of 25 meters but become noticeable at a distance of 100 meters or more. Further, the angle between the gun and the microphone also plays a role, since the microphone has a directional characteristic.
A third set of challenges to effective gunshot detection and categorization analysis is effects of variability in recording devices. In Freytag, J. C., and Brustad, B. M., “A survey of audio forensic gunshot investigations,” Proc. AES 12th International Conf., Audio Forensics in the Digital Age, pp. 131-134, July 2005 (hereinafter “Freytag et al.”), it has been shown that the same weapon with the same ammunition yields significantly different signatures for each recording device. As pointed out in Maher, R. C, “Acoustical characterization of gunshots,” IEEE SAFE 2007, gunshots are impulse-like signals and therefore the signatures are as informative of the overall capture conditions as they are of the nature of the gunshot.
Past work in audio classification has centered on classifying broad categories such as speech, music, cheering, etc., using Gaussian Mixture Models (GMM's) and Hidden Markov Models (HMM's) as described in Otsuka, I, Shipman, S and Divakaran, A., “A Video-Browsing Enabled Personal Video Recorder,” in Multimedia Content Analysis: Theory and Applications, Editor Ajay Divakaran, Springer 2008, and as described in Smaragdis, P, Radhakrishnan, R, Wilson, K., “Context Extraction through Audio Signal Analysis,” in Multimedia Content Analysis: Theory and Applications, Editor Ajay Divakaran, Springer 2008. Such broad classification schemes have sufficed for audio-visual event detection applications such as consumer video browsing and surveillance. However, these schemes fall short when a finer characterization of gunshots into precise weapon categories is needed. Clavel, C. Ehrette, T. Richard, G., “Events Detection for an Audio-Based Surveillance System,” IEEE International Conference on Multimedia and Expo, ICME 2005, come closest to employing a fine classification scheme by detecting and classifying gunshots using a collection of sub-classifiers for guns, grenades, etc. Other prior work in gunshot analysis such as is described in Freytag, J. C., and Brustad, B. M., “A survey of audio forensic gunshot investigations,” Proc. AES 12th International Conf., Audio Forensics in the Digital Age, pp. 131-134, July 2005 has been based on a non-hierarchical template matching over various weapon types. The main disadvantage of non-hierarchical approaches is that they are time consuming, since characterization of a given acoustic signature requires searching an entire database of weapons. Secondly, these approaches require that acoustic capture conditions be consistent across training and testing gunshot samples. This constraint limits the applicability of weapon identification to controlled laboratory conditions or preselected environmental conditions.
Circumventing the problems described above requires a canonical space of weapon signatures that can act as a bridge between different recording conditions and that is favorable to a hierarchical course-to-fine analysis of weapon acoustic signatures (e.g., from broad categories to more detailed categories). With course-to-fine hierarchical approaches, it is not necessary to search an entire database, but only a form of a tree search, thereby constituting a dimensionality reduction approach. Unfortunately, the data driven nature of prior art dimensional/hierarchical methods such as principle component analysis (PCA) renders it difficult if not impossible to make correspondence between the dimensions in one space to another space.
It is desirable to employ a family of models trained on a suitable variety of recording devices, with a model for each recording device. If a wide enough variety of recording devices are used, at least one recording device is likely to be acceptably close to the actual recording device that captures a particular gunshot noise, and thus find a matching weapon. At the same time, it is also desirable to reduce the size of the set of recoding devices and gunshot sample recording types and conditions to be searched and compared.
Accordingly, what would be desirable, but has not yet been provided, is a system and method to automatically detect and classify firearm types across different recording conditions using a small set of exemplars (gunshot waveform types and acoustical conditions).